Quantitative Analysis Model of Complex Communication Networks

Abstract

The characteristics of communication among the members of an organization can help to identify different organizational processes. In a series of studies, we applied information theory and the Markov chain to construct quantitative models capable of determining the information distribution for infinitely repeated communication in an organization. However, the constructed models primarily focus on randomness in information processing and do not sufficiently address the concept of fuzziness. The dual ambiguity of randomness and fuzziness exists in human information processing, neither of which can be overlooked when discussing organizational communication. In this study, we considered a memory communication channel and proposed an estimation model that included randomness and fuzziness to analyze the prior information allocation ratio of each of the members in two organizations. In addition, we analyzed a communication network with isomorphic and non-isomorphic structures. The proposed model, particularly in the field of organizational theory, will provide new research perspectives that incorporate randomness and fuzziness of information in human information processing and organizational communication. Furthermore, the proposed model suggests the possibility of designing or restructuring an organization that uniformly distributes the amount of prior information to a single organization or multiple ones.